Overview:
The Challenge: Companies using AWS Bedrock or Google Vertex AI for RAG solutions were getting hallucinations and inconsistent results—but had no visibility into the root cause. Was it the model? The prompts? The document parsing?
The Solution: A diagnostic platform that lets teams upload documents, test different LLM models and prompts, and compare outputs side-by-side to pinpoint exactly what's going wrong—before committing to an expensive production deployment.
Timeline
5 Day Build
2024
Industry
AI / Tech
Enterprise
Tech Stack
React / Python
AI Enabled
YES
Tools & Technologies
System Architecture
Diagnostic Interface
Debug mode active
Key Features
7 diagnostic modules designed to eliminate guesswork from your RAG implementation.
Model Selection
Compare GPT-4, Claude, Llama, and more side-by-side.
Prompt Testing
Iterate on system prompts in real-time with instant feedback.
Side-by-Side
Compare model responses visually to spot differences.
Custom Thresholds
Define your own accuracy metrics and benchmarks.
Document Upload
Test against your actual knowledge base documents.
Eval Reports
Exportable analysis and recommendations for stakeholders.
Tracing
Debug and trace model responses step-by-step.
Cost Analysis
Estimate token costs before committing to production.
Model Selection
Compare GPT-4, Claude, Llama, and more side-by-side.
Prompt Testing
Iterate on system prompts in real-time with instant feedback.
Side-by-Side
Compare model responses visually to spot differences.
Custom Thresholds
Define your own accuracy metrics and benchmarks.
Document Upload
Test against your actual knowledge base documents.
Eval Reports
Exportable analysis and recommendations for stakeholders.
Tracing
Debug and trace model responses step-by-step.
Cost Analysis
Estimate token costs before committing to production.
Platform Walkthrough
Visual Interface GalleryLLM Selection
Choose from multiple language models and configure parameters like temperature, max tokens, and system prompts.
Test Configuration
Set up evaluation criteria, upload test documents, and define success metrics for your specific use case.
Fine-Tune Settings
Adjust model parameters and compare how different configurations affect output quality and accuracy.
Debug Mode
Step through the RAG pipeline to see exactly how documents are retrieved and how responses are generated.
Built for clarity.
This isn't another RAG tutorial. It's a production-grade diagnostic tool that saves enterprises thousands in wasted API calls and months of trial-and-error.
We finally understood why our RAG pipeline was hallucinating. Saved us from a costly production mistake.
— Enterprise AI Team